Sustainable and Efficient AI: Recent Trends in Resource Management and Public Health Impacts

The recent advancements in the research area primarily revolve around enhancing the efficiency and sustainability of AI applications, particularly in the context of energy consumption and public health impacts. There is a notable shift towards integrating large language models (LLMs) with various domains to improve decision-making processes, exemplified by frameworks like GreenQA for green building design and QUASAR for question answering over heterogeneous data. These innovations not only streamline operations but also reduce the learning curve and computational costs associated with traditional methods.

Sustainability remains a critical concern, with studies like the 'Survey of Sustainability in Large Language Models' and 'The Unpaid Toll: Quantifying the Public Health Impact of AI' highlighting the environmental and health implications of AI's energy-intensive operations. Efforts are being made to quantify and mitigate these impacts through methodologies that assess lifecycle emissions and public health burdens, as well as through the development of energy-efficient AI systems.

Resource management and monitoring tools, such as CEEMS and EaCO, are emerging to address the growing energy demands of AI workloads by providing real-time energy and emissions monitoring, and by optimizing resource allocation and scheduling to enhance energy efficiency. These tools are crucial for maintaining performance while reducing the environmental footprint of AI operations.

In summary, the field is progressing towards more sustainable and efficient AI solutions, with a strong emphasis on integrating LLMs into practical applications, quantifying and mitigating the environmental and health impacts of AI, and developing tools for better resource management. Notable papers include 'GreenQA: A Multimodal Data Reasoning Method Driven by Large Language Models' for its innovative integration of LLMs in green building design, and 'The Unpaid Toll: Quantifying the Public Health Impact of AI' for its comprehensive methodology in assessing AI's public health burden.

Sources

Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models

A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges

The Unpaid Toll: Quantifying the Public Health Impact of AI

CEEMS: A Resource Manager Agnostic Energy and Emissions Monitoring Stack

RAG-based Question Answering over Heterogeneous Data and Text

Piece of Table: A Divide-and-Conquer Approach for Selecting Sub-Tables in Table Question Answering

DocSum: Domain-Adaptive Pre-training for Document Abstractive Summarization

EaCO: Resource Sharing Dynamics and Its Impact on Energy Efficiency for DNN Training

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization

Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node

Reasoning-Aware Query-Focused Summarization over Multi-Table Data

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